Towards Personalized Similarity Search for Vector Databases

D 2024

Towards Personalized Similarity Search for Vector Databases

MAHRÍK, Marek, Matúš ŠIKYŇA, Vladimír MÍČ and Pavel ZEZULA

Basic information

Original name

Towards Personalized Similarity Search for Vector Databases

Authors

MAHRÍK, Marek, Matúš ŠIKYŇA, Vladimír MÍČ and Pavel ZEZULA

Edition

Cham, 17th International Conference on Similarity Search and Applications (SISAP 2024), p. 126-139, 14 pp. 2024

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

Organization unit

Faculty of Informatics

ISBN

978-3-031-75822-5

DOI

http://dx.doi.org/10.1007/978-3-031-75823-2_11

Keywords in English

Similarity search;Personalized similarity;Vector databases

Abstract

V originále

The importance of similarity search has become prominent in the fast-evolving vector databases, which apply content embedding techniques on complex data to produce and manage large collections of high-dimensional vectors. Processing of such data is only possible by using a similarity function for storage, structure, and retrieval. However, if multiple users access the collection, their views on similarity can differ as similarity, in general, is subjective and context-dependent. In this article, we elaborate on the problem of a similarity search engine implementation, where users use a common index but search with personalised views of similarity, implemented by a possibly different similarity model. Specifically, we define a foundational theoretical framework and conduct experiments on real-life data to confirm the viability of such an approach. The experiments also indicate future research directions needed to propose and implement an effective and efficient personalised similarity search engine.

Links

MUNI/A/1590/2023, interní kód MU

Name: Využití technik umělé inteligence pro zpracování dat, komplexní analýzy a vizualizaci rozsáhlých dat

Investor: Masaryk University, Using artificial intelligence techniques for data processing, complex analysis and visualization of large-scale data

Citovat

MAHRÍK, Marek, Matúš ŠIKYŇA, Vladimír MÍČ and Pavel ZEZULA. Towards Personalized Similarity Search for Vector Databases. In 17th International Conference on Similarity Search and Applications (SISAP 2024). Cham: Springer, 2024, p. 126-139. ISBN 978-3-031-75822-5. Available from: https://dx.doi.org/10.1007/978-3-031-75823-2_11.

@inproceedings{2429637,
   author = {Mahrík, Marek and Šikyňa, Matúš and Míč, Vladimír and Zezula, Pavel},
   address = {Cham},
   booktitle = {17th International Conference on Similarity Search and Applications (SISAP 2024)},
   doi = {http://dx.doi.org/10.1007/978-3-031-75823-2_11},
   keywords = {Similarity search;Personalized similarity;Vector databases},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-031-75822-5},
   pages = {126-139},
   publisher = {Springer},
   title = {Towards Personalized Similarity Search for Vector Databases},
   year = {2024}
}

TY  - JOUR
ID  - 2429637
AU  - Mahrík, Marek - Šikyňa, Matúš - Míč, Vladimír - Zezula, Pavel
PY  - 2024
TI  - Towards Personalized Similarity Search for Vector Databases
PB  - Springer
CY  - Cham
SN  - 9783031758225
KW  - Similarity search;Personalized similarity;Vector databases
N2  - The importance of similarity search has become prominent in the fast-evolving vector databases, which apply content embedding techniques on complex data to produce and manage large collections of high-dimensional vectors. Processing of such data is only possible by using a similarity function for storage, structure, and retrieval. However, if multiple users access the collection, their views on similarity can differ as similarity, in general, is subjective and context-dependent. In this article, we elaborate on the problem of a similarity search engine implementation, where users use a common index but search with personalised views of similarity, implemented by a possibly different similarity model. Specifically, we define a foundational theoretical framework and conduct experiments on real-life data to confirm the viability of such an approach. The experiments also indicate future research directions needed to propose and implement an effective and efficient personalised similarity search engine.
ER  -

MAHRÍK, Marek, Matúš ŠIKYŇA, Vladimír MÍČ and Pavel ZEZULA. Towards Personalized Similarity Search for Vector Databases. In \textit{17th International Conference on Similarity Search and Applications (SISAP 2024)}. Cham: Springer, 2024, p.~126-139. ISBN~978-3-031-75822-5. Available from: https://dx.doi.org/10.1007/978-3-031-75823-2\_{}11.

Detailed Information on Publication Record